KR101923962B1 - Method for facilitating medical image view and apparatus using the same - Google Patents

Method for facilitating medical image view and apparatus using the same Download PDF

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KR101923962B1
KR101923962B1 KR1020180016636A KR20180016636A KR101923962B1 KR 101923962 B1 KR101923962 B1 KR 101923962B1 KR 1020180016636 A KR1020180016636 A KR 1020180016636A KR 20180016636 A KR20180016636 A KR 20180016636A KR 101923962 B1 KR101923962 B1 KR 101923962B1
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image
lesion
interest
images
computing device
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Korean (ko)
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박현호
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주식회사 뷰노
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

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Abstract

The present invention relates to a method for supporting opening a medical image and an apparatus using the same. According to the present invention, the method enables a computing apparatus to open a medical image based on a time axis for a series set in response to a specific input of an input apparatus, and respective images included in the series set are aligned with respect to a predetermined lesion of interest, so the lesions of interest in the respective images provided for opening are positioned at the same visual position.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a medical image viewing method,

The present invention relates to a method for supporting reading of a medical image and an apparatus using the same. Specifically, in accordance with the method of the present invention, in response to a specific input of an input device, a computing device enables viewing on a time axis basis for a time series set of medical images, By aligning on a given lesion of interest, the lesions of interest in the individual images provided for viewing come to the same visual position with respect to each other.

For example, when a plurality of related slice images such as a CT (computed tomography) image are used, a user who is a doctor or the like Through the operation of the input device, the individual slice images are quickly transferred to the adjacent slice images, and the presence or absence of the lesions and their states are generally checked.

For example, a medical image such as a chest CT image widely used for analyzing a lesion to be used for diagnosis is frequently used for reading because it can observe an abnormality in the inside of the body such as lung, bronchus, and heart. Generally, the reading of such a chest CT image proceeds in such a manner that a series of individual slice images are switched from the lowermost or topmost portion of the imaging region to the same window in accordance with the three-dimensional characteristic, A set of time-series medical images, that is, a time series set, in which lesions are photographed, are configured to be read in a separate space from each other.

In such a user interface, the user can not concentrate sufficiently on changes in the same progressive attention lesion, which leads to a decrease in the read productivity of the user.

Therefore, in order to solve such a problem, the present invention provides a user interface that enables a time series set of medical images for a progressive lesion to be read in the same space, thereby improving the efficiency in reading out the entire time series set, And a device using the same.

US 7486812 B

 Ignacio Rocco, Relja Arandjelovic, Josef Sivic. Convolutional neural network architecture for geometric matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

The object of the present invention is to make it possible to more closely read the temporal change with respect to the lesion having high importance in the medical image and to improve the efficiency of the reading by displaying the same lesion at the same position even when the image is switched do.

Specifically, the present invention aims at enabling a user, particularly a medical practitioner, to focus on lesions that are substantially required to be read by performing alignment, matching, and / or interpolation between images for computer or human-recognized progressive lesions.

As a result, it is an object of the present invention to improve the efficiency of medical image reading so that the user can check more images in less time, and in particular, to obtain accurate diagnosis results in the progressive lesion readings in medical images The aim is to assist medical personnel to improve the accuracy of analysis.

The characteristic configuration of the present invention for achieving the object of the present invention as described above and realizing the characteristic effects of the present invention described below is as follows.

According to an aspect of the present invention there is provided a method of supporting viewing of a medical image, the method comprising: in response to a particular input of an input device, causing a computing device to generate a time- Wherein the individual images belonging to the time series set are aligned on the basis of a predetermined lesion of interest so that lesions of interest in the individual images provided for browsing come to the same visual position.

According to another aspect of the present invention, there is also provided a computer program stored in a machine readable non-transitory medium, comprising instructions embodied to perform a medical image viewing assist method in accordance with the present invention.

According to still another aspect of the present invention, there is provided a computing apparatus for supporting reading of a medical image, the computing apparatus comprising: a communication unit for obtaining a specific input of an input device; And a processor capable of viewing on a time axis basis for a time series set of medical images in response to the particular input or enabling other devices to be interlocked via the communication section, To align or align individual images belonging to the time series set with respect to each other, so that the lesions of interest in the individual images provided for viewing come to the same visual position.

According to the present invention, it is possible to continuously observe a visual change of a lesion of interest in the same viewing space, thereby enabling a user to more effectively observe and evaluate a change in a lesion of interest.

Therefore, according to the present invention, the efficiency of browsing is improved, so that the doctor can obtain a more accurate diagnosis result in less time in the medical field. As a result, the speed and quality of the reading are improved and the workflow ) Is the ultimate effect of being able to innovate.

In addition, the present invention can be applied to various time-series images, and can be used as it is for a system such as a time-lapse medical image conventionally used in a hospital, for example, a three-dimensionally acquired ultrasound image or an MRI image. , It is needless to say that the method of the present invention is not dependent on a particular type of image or platform.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, The figures can be obtained.
1 is a conceptual diagram schematically illustrating an exemplary configuration of a computing device that performs a method for supporting reading of a medical image according to the present invention (hereinafter referred to as a "medical image reading support method").
FIG. 2 is an exemplary block diagram illustrating hardware or software components of a computing device that performs a medical image viewing support method in accordance with the present invention.
3 is a flowchart illustrating an exemplary method for supporting a medical image viewing according to the present invention.
FIG. 4 is a diagram illustrating an example of a time series set displayed according to the medical image viewing support method of the present invention.
FIG. 5 is a conceptual diagram illustrating a method of switching between images according to the medical image viewing support method of the present invention with respect to the time series set shown in FIG.

The following detailed description of the invention refers to the accompanying drawings, which illustrate, by way of example, specific embodiments in which the invention may be practiced in order to clarify the objects, technical solutions and advantages of the invention. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention.

The term "image" or "image data" used throughout the description and claims of the present invention refers to multidimensional data composed of discrete image elements (e.g., pixels in a two-dimensional image and voxels in a three- Quot;

For example, "imaging" may be computed by (cone-beam) computed tomography, magnetic resonance imaging (MRI), ultrasound, or any other medical imaging system known in the art The subject may be a medical image of the subject. The images may also be provided in a non-medical context, for example, a remote sensing system, an electron microscopy, and the like.

Throughout the description and claims of the present invention, an 'image' refers to an image that is visible (eg, displayed on a video screen) or an image (eg, a file corresponding to a pixel output, such as a CT or MRI detector) It is a term referring to a digital representation.

For convenience of illustration, cone-beam computed tomography (CBCT) image data is sometimes shown as an exemplary image modality in the drawings. However, those skilled in the art will appreciate that image formats used in various embodiments of the present invention may be used in various imaging formats such as X-ray imaging, MRI, CT, positron emission tomography (PET), PET-CT, SPECT, SPECT-CT, MR- But it should be understood that the invention is not limited to the examples enumerated.

Throughout the detailed description and claims of the present invention, the term 'DICOM' (Digital Imaging and Communications in Medicine) is a generic term for various standards used in digital image representation and communication in medical devices, The standard is presented at the Joint Committee composed of the American Radiation Medical Association (ACR) and the American Electrical Manufacturers Association (NEMA).

In addition, the term 'Picture Archiving and Communication System (PACS)' refers to a system for storing, processing and transmitting according to the DICOM standard throughout the detailed description and claims of the present invention, , And MRI can be stored in the DICOM format and transmitted to a terminal inside or outside the hospital through the network, and the result of reading and the medical record can be added to the terminal.

Throughout the detailed description and claims of the present invention, 'learning' or 'learning' refers to performing machine learning through computing according to a procedure, It will be understood by those of ordinary skill in the art that the present invention is not intended to be so-called.

And throughout the description and claims of this invention, the word 'comprise' and variations thereof are not intended to exclude other technical features, additions, elements or steps. Also, 'one' or 'one' is used in more than one meaning, and 'another' is limited to at least the second.

Other objects, advantages and features of the present invention will become apparent to those skilled in the art from this description, and in part from the practice of the invention. The following examples and figures are provided by way of illustration and are not intended to limit the invention. Accordingly, the details disclosed herein with respect to a particular structure or function are not to be construed in a limiting sense, but merely as being representative of the general inventive concept providing a guideline for carrying out the invention in various detail structures, It should be interpreted as basic data.

Moreover, the present invention encompasses all possible combinations of embodiments shown herein. It should be understood that the various embodiments of the present invention are different, but need not be mutually exclusive. For example, certain features, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in connection with one embodiment. It should also be understood that the position or arrangement of individual components within each disclosed embodiment may be varied without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is to be limited only by the appended claims, along with the full scope of equivalents to which such claims are entitled, if properly explained. In the drawings, like reference numerals refer to the same or similar functions throughout the several views.

Unless otherwise indicated herein or clearly contradicted by context, items referred to in the singular are intended to encompass a plurality unless otherwise specified in the context. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily carry out the present invention.

1 is a conceptual diagram schematically illustrating an exemplary configuration of a computing device that performs a medical image viewing support method according to the present invention.

1, a computing device 100 according to an embodiment of the present invention includes a communication unit 110 and a processor 120. The communication unit 110 communicates with an external computing device (not shown) Communication is possible.

In particular, the computing device 100 may be implemented as a computer-readable medium, such as conventional computer hardware (e.g., a computer processor, memory, storage, input and output devices, Electronic communication devices, electronic information storage systems such as network-attached storage (NAS) and storage area networks (SAN), and computer software (i.e., computing devices that enable a computing device to function in a particular manner) Commands) to achieve the desired system performance.

The communication unit 110 of the computing device can send and receive requests and responses to and from other interworking computing devices. As an example, such requests and responses can be made by the same transmission control protocol (TCP) session But not limited to, a user datagram protocol (UDP) datagram, for example. In addition, in a broad sense, the communication unit 110 may include a keyboard, a mouse, an external input device, a printer, a display, and other external output devices for receiving commands or instructions.

The processor 120 of the computing device may also be a micro processing unit (MPU), a central processing unit (CPU), a graphics processing unit (GPU), or a tensor processing unit (TPU), a cache memory, a data bus ). ≪ / RTI > It may further include a software configuration of an operating system and an application that performs a specific purpose.

FIG. 2 is an exemplary block diagram illustrating hardware or software components of a computing device that performs a medical image viewing support method in accordance with the present invention.

2, the computing device 100 may include an image acquisition module 210 as a component thereof. The image acquisition module 210 is configured to obtain a time series set of medical images to which the method according to the present invention is applied, wherein the individual modules shown in FIG. 2 are, for example, 110, the processor 120, or the communication unit 110 and the processor 120, as will be understood by those skilled in the art. Here, the time-series set of medical images includes a baseline image, which is the first acquired image for the subject, and a follow-up image, which is an additional acquired image thereafter. This is because many cases of progressive lesions in the medical field are photographed with the same imaging technique at successive time intervals. However, there is no need to be photographed by the same imaging technique, so that images photographed by different imaging techniques can be simultaneously viewed as a time-series set through matching with alignment and size adjustment.

The time series set may be obtained, for example, from an external image storage system such as an imaging device or a medical image storage and transmission system (PACS) interlocked through the communication unit 110, but is not limited thereto. For example, a time series set may be captured by a medical imaging device and transmitted to a PACS in accordance with the DICOM standard, and then acquired by the image acquisition module 210 of the computing device 100.

The acquired individual images may then be delivered to a lesion determination module 220 which may be configured to detect suspicious lesions for each individual image belonging to a time series set have. Alternatively, the lesion judgment module 220 may provide a user with one of the individual images, so that a suspected lesion displayed on the image may be designated by the user. In the case where a suspected lesion is determined by the lesion judgment module 220, the importance of the suspected lesion, for example, the suspected lesion detected in the individual image is determined as the actual lesion reliability, A lesion of interest to be subjected to the subsequent sorting may be selected according to a value calculated based on a score of malignancy or the like of the lesion or importance factors including at least one of reliability and maliciousness. This can be done by the lesion judgment module 220.

One example of the judgment model used in the lesion judgment module 220 is a deep learning model, which can be briefly described as a form in which artificial neural networks are stacked in layers. In other words, it is expressed as a deep neural network (deep neural network) in the sense of a network of deep structure. By learning a large amount of data in a structure composed of a multi-layer network, the characteristics of each image are automatically learned, It is a form that learns the network in a way that minimizes the error of the objective function, that is, the accuracy of judgment of the lesion. This is compared to the connections between neurons in the human brain, and such a neural network is becoming a next generation model of AI. Among these deep learning models, CNN (Convolutional Neural Network) is a model suitable for classification of images. It is a convolutional neural network that generates a feature map by using a plurality of filters for each region of an image. layer and feature map by reducing the size of the sub-sampling layer to extract features that are invariant to changes in position or rotation by repeating the low-level features such as point, line, face, complex and meaning It is possible to extract features of various levels up to the high level features of the present invention and to use the extracted features as the input values of the existing judgment model, it is possible to construct a judgment model with higher accuracy.

However, it will be appreciated by those of ordinary skill in the art that various types of machine learning models or statistical models may be used, as the lesion determination model is not limited to such CNNs.

When a lesion of interest is detected by the lesion determination module 220 or is designated by the user, individual images belonging to the temporal set are delivered to the alignment module 230, and the alignment module 230 determines whether the lesions of interest are at the same visual position Lt; / RTI > To do this, individual images may be offset on a plane.

It will be appreciated that alignment by the alignment module 230 may involve registration, which may involve scaling of the registration.

On the other hand, the individual image is transmitted to the output module 240, and the output module 240 outputs one image among the images belonging to the time series set through a predetermined output device, for example, a user interface displayed on the display, entity.

Here, the external entity includes a user of the computing device 100, a manager, a medical professional in charge of the subject, and the like. In addition, the external entity includes the time-series set, the individual images belonging thereto, Information related to the determination, etc.), it should be understood that any subject is included.

In addition, the input module 250 is configured to allow the current viewing image displayed on the output module 240 to be switched or adjusted in response to a specific input or a predetermined operation.

Specific functions and effects of the respective components shown in FIG. 2 will be described later in detail. Although the components shown in FIG. 2 are illustrated as being realized in one computing device for convenience of explanation, it will be understood that the computing device 100 performing the method of the present invention may be configured such that a plurality of devices are interlocked with each other.

An embodiment of a medical image viewing support method according to the present invention will now be described in more detail with reference to FIGS.

FIG. 3 is a flowchart illustrating a medical image viewing support method according to an embodiment of the present invention. FIG. 4 is a diagram illustrating an example of a time series set displayed according to the medical image viewing support method of the present invention.

Referring to FIG. 3, a method for supporting a medical image viewing according to the present invention is a method for allowing a computing device to view a time series set of a medical image based on a time axis, in response to a specific input of an input device , The individual images belonging to the time series set are aligned on the basis of a predetermined lesion of interest so that lesions of interest in the individual images provided for browsing come to the same visual position.

As used herein, the specific input may include, for example, the number of manipulations commonly used to switch the image, such as wheel rotation of the mouse, dragging of the mouse or touch pad, input of pressing an arrow key on the keyboard, However, special keys such as 'Ctrl' and 'Alt' keys may be combined to distinguish the slice images photographed at the same point from the spatial conversion. Such a particular input has characteristics that are repeatedly input, which can be measured by the input amount. For example, the rotation of the wheel of the mouse is designed so that an intended action is performed when an input amount repeatedly accumulated reaches a certain value, and in the present invention, such an action is an action of switching between images.

3, the medical image viewing support method according to the present invention includes: an image acquisition module 210 implemented by the computing device 100 acquires a time series set of the medical image, (Step S100), which may be a CT image in which the same lesion of interest appears as exemplarily shown in Fig. The lesion of interest shown in Fig. 4 is indicated by an arrow.

It will be appreciated, however, that the present invention is not limited to the lesion or image type illustrated, but can generally be applied to various types of lesions and image types.

The medical image viewing support method according to the present invention is a method for supporting the medical image viewing according to the present invention in which the output module 240 implemented by the computing device 100 generates a baseline image and a follow up image (S200) of presenting, as a current browse image which is an individual image provided in the current browsing, or providing the other device with the present one. For example, the one image may be an image photographed at the earliest one of the individual images belonging to the time series set, for example, a reference image, or an image photographed at the latest time.

3, the medical image viewing support method according to the present invention is a method for supporting the medical image viewing according to the present invention, in which the computing device 100 determines whether or not the image to be provided as the current browsed image on the basis of the directionality corresponding to the specific input, (S400) repeatedly updating the next browse image which is an individual image.

Here, the directionality corresponding to a specific input refers to a criterion for determining whether the specific input is to convert the current browsed image to a previous image or to switch to the next image. For example, Pressing the key switches to the previous image, and pressing the right arrow key switches to the next image, which is related to the intended viewing direction of the particular input. For a mouse, it can be determined according to the direction of wheel rotation. Here, it is preferable that the relation between the 'current' browse image, the 'previous' image and the 'next' image is determined based on the time axis, that is, the photographing order.

FIG. 5 is a conceptual diagram illustrating a method of switching between images according to the medical image viewing support method of the present invention with respect to the time series set shown in FIG.

Referring to FIG. 5, an example in which the mouse wheel rotation combined with the 'Ctrl' key is utilized as the specific input is shown. According to the specific input, individual images of the time series set including the reference image and the follow- May be provided to be switched to each other in the same area provided on the interface.

In addition, the current browse image and the next browse image in step S400 are aligned with respect to the lesion of interest, so that the lesion of interest in the current browse image and the lesion of interest in the next browse image are at the same visual position Convenience is provided.

For this sort of alignment, prior to step S400, a medical image support method in accordance with the present invention may be performed by a sorting module, implemented by the computing device 100, when the lesion of interest is detected by a lesion determination module, (S310) of aligning or aligning the images to be aligned so that the lesion of interest represented in the images to be aligned, which are at least two individual images including the current browse image, are at the same visual position .

For example, it is understood that the alignment can be performed by a learned deep learning algorithm based on a large amount of training data including a traditional machine learning algorithm or positional information between medical images. Will be.

Such an arrangement may include not only being located at the same visual position but also registration with the adjustment of the size of the images to be aligned so that they appear on the same scale.

For example, matching may be performed by geometric dense matching.

For example, this geometric density matching can be performed in a handcrafted method that takes into account handcrafted features, such as hand-crafted methods, for example, scale-invariant feature transform (SIFT) ), HOG (histogram of oriented gradients), RANSAC (random sample consensus), and Hough transform. These handcraped features are robustness to some extent because they are a long time-finding method by repeated attempts and errors by assumptions made by experts' experiences and analysis.

However, the geometric density matching is not limited to this, and the geometric density matching may be performed by a pre-learned deep learning algorithm based on a large amount of learning data between the similar medical images and the corresponding other medical images It will be understood by those skilled in the art that non-patent document 1: Ignacio Rocco, Relja Arandjelovic, Josef Sivic. Convolutional neural network architecture for geometric matching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, an example thereof is disclosed. When a large number of training data is available, the geometric density matching by the deep learning algorithm can easily outperform the handcrafted method.

By the above-described alignment process, lesions of the same interest appearing on different individual images may come to the same visual position, and the lesions of the same interest may appear in the same accumulation by matching.

On the other hand, in order to smoothly switch the images between adjacent individual images belonging to the time series set, in one embodiment of the present invention, after the step S400, after the step S310, The computing device 100 may support to generate or generate at least one interpolation image interpolating between the images to be aligned in operation S320.

As a result, in the case where a user, for example, a reader, intends to read more intensively, not only can the same interested lesion be conveniently switched along the time axis through the predetermined operation, but also the switching is smoothly performed by interpolation There is an effect that can be.

As described above, the present invention has the effect of confirming the temporal change of the same lesion conveniently and quickly by a simple operation over all of the above-described embodiments and modifications. Accordingly, it is possible to reduce the trouble of reading by the conventional user interface, which is inconvenient, so that diagnosis can be effectively performed. As a result, the quality of the medical treatment can be improved and the workflow in the medical field can be improved.

Based on the description of the embodiments above, those skilled in the art will recognize that the methods and / or processes of the present invention and their steps may be implemented in hardware, software, or any combination of hardware and software suitable for the particular application Points can be clearly understood. The hardware may include special features or components of a general purpose computer and / or a dedicated computing device or a specific computing device or a particular computing device. The processes may be realized by one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices having internal and / or external memory. Additionally or alternatively, the processes can be configured to process application specific integrated circuits (ASICs), programmable gate arrays, programmable array logic (PAL) Or any other device or combination of devices. Furthermore, the objects of the technical solution of the present invention, or portions contributing to the prior art, may be implemented in the form of program instructions that can be executed through various computer components and recorded on a machine-readable recording medium. The machine-readable recording medium may include program commands, data files, data structures, and the like, alone or in combination. The program instructions recorded on the machine-readable recording medium may be those specially designed and constructed for the present invention or may be those known to those of ordinary skill in the computer software arts. Examples of the machine-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROM, DVD, Blu-ray, magneto-optical media such as floptical disks magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include, but are not limited to, any of the above devices, as well as a heterogeneous combination of processors, processor architectures or combinations of different hardware and software, Which may be constructed using a structured programming language such as C, an object-oriented programming language such as C ++ or an advanced or low-level programming language (assembly language, hardware description languages and database programming languages and techniques) This includes not only bytecode, but also high-level language code that can be executed by a computer using an interpreter or the like.

Thus, in one aspect of the present invention, when the methods and combinations described above are performed by one or more computing devices, combinations of the methods and methods may be implemented as executable code that performs each of the steps. In another aspect, the method may be implemented as systems for performing the steps, and the methods may be distributed in various ways throughout the devices, or all functions may be integrated into one dedicated, stand-alone device, or other hardware. In yet another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and / or software described above. All such sequential combinations and combinations are intended to be within the scope of this disclosure.

For example, the hardware device may be configured to operate as one or more software modules to perform processing in accordance with the present invention, and vice versa. The hardware device may include a processor, such as an MPU, CPU, GPU, TPU, coupled to a memory, such as ROM / RAM, for storing program instructions and configured to execute instructions stored in the memory, And a communication unit capable of receiving and sending data. In addition, the hardware device may include a keyboard, a mouse, and other external input devices for receiving commands generated by the developers.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, Those skilled in the art will appreciate that various modifications and changes may be made thereto without departing from the scope of the present invention.

Therefore, the spirit of the present invention should not be construed as being limited to the above-described embodiments, and all of the equivalents or equivalents of the claims, as well as the following claims, I will say.

Such equally or equivalently modified means include, for example, a logically equivalent method which can produce the same result as the method according to the present invention, Should not be limited by the foregoing examples, but should be understood in the broadest sense permissible by law.

Claims (11)

A method of supporting viewing on a time axis basis with respect to a time series set of medical images taken at time intervals in which a temporal change in lesion is possible,
(a) supporting a computing device to acquire the time series set or acquire another device associated with the computing device;
(b) the computing device provides one of a baseline image and a follow-up image belonging to the time-series set as a current browse image, which is a separate image provided at the current browse, on the display Supporting the other device to provide; And
(c) in response to a specific input of the input device, cause the computing device to repeatedly generate an image provided as the current browse image based on the directionality corresponding to the specific input, Steps to update
, ≪ / RTI &
The current browse image and the next browse image are aligned with each other based on a predetermined lesion of interest so that the lesion of interest in the current browse image and the lesion of interest in the next browse image come to the same visual position With features,
Before the step (c)
(c0) if the lesion of interest is detected by a lesion determination module or is designated by a user, the computing device determines whether the lesion of interest represented in the alignment subject images, which are at least two individual images including the current browse image, And aligning or aligning the images to be aligned with each other so as to be in a position
Further comprising:
In the step (c0)
The alignment may include,
Wherein the registration includes registration with the size adjustment of the images to be aligned so that the lesions of interest are located at the same visual position on the same scale with each other. delete delete delete The method according to claim 1,
After step (c0), before step (c)
(c1) supporting the computing device to generate or generate at least one interpolation image interpolated between the images to be aligned
Wherein the medical image viewing support method further comprises: A computer program stored in a machine-readable non-transitory medium, comprising instructions for a computing device to implement the method of any one of claims 1 to 5. A computing device for supporting viewing on a time axis basis with respect to a time-series set of medical images taken at time intervals in which a temporal change in lesion is possible,
A communication unit for acquiring a specific input of the input device; And
A processor capable of enabling viewing on a time axis basis for a time series set of medical images in response to the specific input or enabling other devices to be linked through the communication unit
, ≪ / RTI &
The processor comprising:
By aligning or aligning individual images belonging to the time series set on the basis of a given lesion of interest so that lesions of interest in the individual images provided for viewing come to the same visual position,
Wherein,
To obtain or obtain the time series set,
The processor comprising:
A one of a baseline image and a follow up image belonging to the time series set is provided on a display as a current browse image which is an individual image provided for a current browse through the communication unit, To provide,
An image providing unit configured to repeatedly update an image provided as the current browse image based on a direction corresponding to the specific input in response to a specific input of the input device to a next browse image that is an individual image determined to be provided for a next browse,
The current browse image and the next browse image are aligned with each other based on a predetermined lesion of interest so that the lesion of interest in the current browse image and the lesion of interest in the next browse image come to the same visual position In addition,
Before the iterative update,
The processor comprising:
Wherein when the lesion of interest is detected by a lesion determination module or is designated by a user, the alignment target images are displayed such that the lesion of interest, which is at least two individual images including the current browse image, And aligning or aligning them with each other,
The alignment may include,
Wherein the registration includes registration with the size adjustment of the images to be aligned in order to place the lesions of interest at the same visual position on the same scale. delete delete delete 8. The method of claim 7,
Before the iterative update after the matching,
The processor comprising:
And generates or generates at least one interpolation image interpolated between the images to be aligned.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102283673B1 (en) 2020-11-30 2021-08-03 주식회사 코어라인소프트 Medical image reading assistant apparatus and method for adjusting threshold of diagnostic assistant information based on follow-up exam
WO2023140475A1 (en) * 2022-01-21 2023-07-27 주식회사 루닛 Apparatus and method for linking lesion location between guide image and 3d tomosynthesis images including plurality of 3d image slices, and providing linked image

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102283673B1 (en) 2020-11-30 2021-08-03 주식회사 코어라인소프트 Medical image reading assistant apparatus and method for adjusting threshold of diagnostic assistant information based on follow-up exam
DE102021131242A1 (en) 2020-11-30 2022-06-02 Coreline Soft Co., Ltd Medical image reading assistant apparatus and method for adjusting a diagnostic assistant information threshold based on a follow-up examination
KR20220076283A (en) 2020-11-30 2022-06-08 주식회사 코어라인소프트 Medical image reading assistant apparatus and method for adjusting threshold of diagnostic assistant information based on follow-up exam
US11915822B2 (en) 2020-11-30 2024-02-27 Coreline Soft Co., Ltd. Medical image reading assistant apparatus and method for adjusting threshold of diagnostic assistant information based on follow-up examination
WO2023140475A1 (en) * 2022-01-21 2023-07-27 주식회사 루닛 Apparatus and method for linking lesion location between guide image and 3d tomosynthesis images including plurality of 3d image slices, and providing linked image
US11935236B2 (en) 2022-01-21 2024-03-19 Lunit Inc. Apparatus and method for interlocking lesion locations between a guide image and a 3D tomosynthesis images composed of a plurality of 3D image slices

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